dpfm-factory 0.8.4

Helper scripts for digital pathology foundation models

dpfm_factory

dpfm_factory is a Python package that provides a factory function to easily load different machine learning models and their associated preprocessing pipelines from Hugging Face. This package is particularly useful in the digital and computational pathology domains, where it is crucial to work with various specialized models.

Features

• Easy Model Loading: Load machine learning models and their preprocessing pipelines with a simple factory function.
• Hugging Face Integration: Seamlessly integrates with Hugging Face to authenticate and load models.
• Custom Environment Variables: Supports loading environment variables from a .env file for sensitive data like tokens.

Installation

To install the package directly from GitHub, use the following command:

pip install git+https://github.com/Steven-N-Hart/dpfm_factory

Ensure that you have all necessary dependencies listed in the requirements.txt file. Alternatively, clone the repository and install the package locally:

git clone https://github.com/Steven-N-Hart/dpfm_factory
cd dpfm_factory
pip install -r requirements.txt
pip install .

Usage

Setup

Before using the package, make sure to create a .env file in the root of your project directory with your Hugging Face token:

HUGGINGFACE_TOKEN=your_huggingface_token_here

Example Usage

Here’s an example of how to use the model_factory function to load a model and its associated processor:

from dpfm_factory.model_runners import model_factory

# Specify the model you want to load
model_name = 'MahmoodLab/conch'

# Load the model, processor, and the function to get image embeddings
model, processor, get_image_embedding = model_factory(model_name)

# Example usage with an image (replace 'your_image' with actual image data)
image_embedding = get_image_embedding(your_image)

print("Image Embedding:", image_embedding)

Supported Models

The model_factory function currently supports the following models:

• owkin/phikon
• paige-ai/Virchow2
• MahmoodLab/conch
• prov-gigapath/prov-gigapath
• LGAI-EXAONE/EXAONEPath
• histai/hibou-L
• histai/hibou-b

Error Handling

If an unsupported model name is provided, the model_factory will raise a NotImplementedError. For example:

try:
    model, processor, get_image_embedding = model_factory('unsupported/model_name')
except NotImplementedError as e:
    print(e)

Contributing

Contributions are welcome! Please fork the repository and submit a pull request with your changes.